{
 "cells": [
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "import numpy as np\n",
    "import os\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['axes.labelsize'] = 14\n",
    "plt.rcParams['xtick.labelsize'] = 12\n",
    "plt.rcParams['ytick.labelsize'] = 12\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 树模型的可视化展示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                       max_depth=2, max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
       "                       random_state=None, splitter='best')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "iris = load_iris()\n",
    "X = iris.data[:, 2:]\n",
    "y = iris.target\n",
    "\n",
    "dtc = DecisionTreeClassifier(max_depth=2)\n",
    "dtc.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal length (cm)',\n",
       " 'sepal width (cm)',\n",
       " 'petal length (cm)',\n",
       " 'petal width (cm)']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'data': array([[5.1, 3.5, 1.4, 0.2],\n",
       "        [4.9, 3. , 1.4, 0.2],\n",
       "        [4.7, 3.2, 1.3, 0.2],\n",
       "        [4.6, 3.1, 1.5, 0.2],\n",
       "        [5. , 3.6, 1.4, 0.2],\n",
       "        [5.4, 3.9, 1.7, 0.4],\n",
       "        [4.6, 3.4, 1.4, 0.3],\n",
       "        [5. , 3.4, 1.5, 0.2],\n",
       "        [4.4, 2.9, 1.4, 0.2],\n",
       "        [4.9, 3.1, 1.5, 0.1],\n",
       "        [5.4, 3.7, 1.5, 0.2],\n",
       "        [4.8, 3.4, 1.6, 0.2],\n",
       "        [4.8, 3. , 1.4, 0.1],\n",
       "        [4.3, 3. , 1.1, 0.1],\n",
       "        [5.8, 4. , 1.2, 0.2],\n",
       "        [5.7, 4.4, 1.5, 0.4],\n",
       "        [5.4, 3.9, 1.3, 0.4],\n",
       "        [5.1, 3.5, 1.4, 0.3],\n",
       "        [5.7, 3.8, 1.7, 0.3],\n",
       "        [5.1, 3.8, 1.5, 0.3],\n",
       "        [5.4, 3.4, 1.7, 0.2],\n",
       "        [5.1, 3.7, 1.5, 0.4],\n",
       "        [4.6, 3.6, 1. , 0.2],\n",
       "        [5.1, 3.3, 1.7, 0.5],\n",
       "        [4.8, 3.4, 1.9, 0.2],\n",
       "        [5. , 3. , 1.6, 0.2],\n",
       "        [5. , 3.4, 1.6, 0.4],\n",
       "        [5.2, 3.5, 1.5, 0.2],\n",
       "        [5.2, 3.4, 1.4, 0.2],\n",
       "        [4.7, 3.2, 1.6, 0.2],\n",
       "        [4.8, 3.1, 1.6, 0.2],\n",
       "        [5.4, 3.4, 1.5, 0.4],\n",
       "        [5.2, 4.1, 1.5, 0.1],\n",
       "        [5.5, 4.2, 1.4, 0.2],\n",
       "        [4.9, 3.1, 1.5, 0.2],\n",
       "        [5. , 3.2, 1.2, 0.2],\n",
       "        [5.5, 3.5, 1.3, 0.2],\n",
       "        [4.9, 3.6, 1.4, 0.1],\n",
       "        [4.4, 3. , 1.3, 0.2],\n",
       "        [5.1, 3.4, 1.5, 0.2],\n",
       "        [5. , 3.5, 1.3, 0.3],\n",
       "        [4.5, 2.3, 1.3, 0.3],\n",
       "        [4.4, 3.2, 1.3, 0.2],\n",
       "        [5. , 3.5, 1.6, 0.6],\n",
       "        [5.1, 3.8, 1.9, 0.4],\n",
       "        [4.8, 3. , 1.4, 0.3],\n",
       "        [5.1, 3.8, 1.6, 0.2],\n",
       "        [4.6, 3.2, 1.4, 0.2],\n",
       "        [5.3, 3.7, 1.5, 0.2],\n",
       "        [5. , 3.3, 1.4, 0.2],\n",
       "        [7. , 3.2, 4.7, 1.4],\n",
       "        [6.4, 3.2, 4.5, 1.5],\n",
       "        [6.9, 3.1, 4.9, 1.5],\n",
       "        [5.5, 2.3, 4. , 1.3],\n",
       "        [6.5, 2.8, 4.6, 1.5],\n",
       "        [5.7, 2.8, 4.5, 1.3],\n",
       "        [6.3, 3.3, 4.7, 1.6],\n",
       "        [4.9, 2.4, 3.3, 1. ],\n",
       "        [6.6, 2.9, 4.6, 1.3],\n",
       "        [5.2, 2.7, 3.9, 1.4],\n",
       "        [5. , 2. , 3.5, 1. ],\n",
       "        [5.9, 3. , 4.2, 1.5],\n",
       "        [6. , 2.2, 4. , 1. ],\n",
       "        [6.1, 2.9, 4.7, 1.4],\n",
       "        [5.6, 2.9, 3.6, 1.3],\n",
       "        [6.7, 3.1, 4.4, 1.4],\n",
       "        [5.6, 3. , 4.5, 1.5],\n",
       "        [5.8, 2.7, 4.1, 1. ],\n",
       "        [6.2, 2.2, 4.5, 1.5],\n",
       "        [5.6, 2.5, 3.9, 1.1],\n",
       "        [5.9, 3.2, 4.8, 1.8],\n",
       "        [6.1, 2.8, 4. , 1.3],\n",
       "        [6.3, 2.5, 4.9, 1.5],\n",
       "        [6.1, 2.8, 4.7, 1.2],\n",
       "        [6.4, 2.9, 4.3, 1.3],\n",
       "        [6.6, 3. , 4.4, 1.4],\n",
       "        [6.8, 2.8, 4.8, 1.4],\n",
       "        [6.7, 3. , 5. , 1.7],\n",
       "        [6. , 2.9, 4.5, 1.5],\n",
       "        [5.7, 2.6, 3.5, 1. ],\n",
       "        [5.5, 2.4, 3.8, 1.1],\n",
       "        [5.5, 2.4, 3.7, 1. ],\n",
       "        [5.8, 2.7, 3.9, 1.2],\n",
       "        [6. , 2.7, 5.1, 1.6],\n",
       "        [5.4, 3. , 4.5, 1.5],\n",
       "        [6. , 3.4, 4.5, 1.6],\n",
       "        [6.7, 3.1, 4.7, 1.5],\n",
       "        [6.3, 2.3, 4.4, 1.3],\n",
       "        [5.6, 3. , 4.1, 1.3],\n",
       "        [5.5, 2.5, 4. , 1.3],\n",
       "        [5.5, 2.6, 4.4, 1.2],\n",
       "        [6.1, 3. , 4.6, 1.4],\n",
       "        [5.8, 2.6, 4. , 1.2],\n",
       "        [5. , 2.3, 3.3, 1. ],\n",
       "        [5.6, 2.7, 4.2, 1.3],\n",
       "        [5.7, 3. , 4.2, 1.2],\n",
       "        [5.7, 2.9, 4.2, 1.3],\n",
       "        [6.2, 2.9, 4.3, 1.3],\n",
       "        [5.1, 2.5, 3. , 1.1],\n",
       "        [5.7, 2.8, 4.1, 1.3],\n",
       "        [6.3, 3.3, 6. , 2.5],\n",
       "        [5.8, 2.7, 5.1, 1.9],\n",
       "        [7.1, 3. , 5.9, 2.1],\n",
       "        [6.3, 2.9, 5.6, 1.8],\n",
       "        [6.5, 3. , 5.8, 2.2],\n",
       "        [7.6, 3. , 6.6, 2.1],\n",
       "        [4.9, 2.5, 4.5, 1.7],\n",
       "        [7.3, 2.9, 6.3, 1.8],\n",
       "        [6.7, 2.5, 5.8, 1.8],\n",
       "        [7.2, 3.6, 6.1, 2.5],\n",
       "        [6.5, 3.2, 5.1, 2. ],\n",
       "        [6.4, 2.7, 5.3, 1.9],\n",
       "        [6.8, 3. , 5.5, 2.1],\n",
       "        [5.7, 2.5, 5. , 2. ],\n",
       "        [5.8, 2.8, 5.1, 2.4],\n",
       "        [6.4, 3.2, 5.3, 2.3],\n",
       "        [6.5, 3. , 5.5, 1.8],\n",
       "        [7.7, 3.8, 6.7, 2.2],\n",
       "        [7.7, 2.6, 6.9, 2.3],\n",
       "        [6. , 2.2, 5. , 1.5],\n",
       "        [6.9, 3.2, 5.7, 2.3],\n",
       "        [5.6, 2.8, 4.9, 2. ],\n",
       "        [7.7, 2.8, 6.7, 2. ],\n",
       "        [6.3, 2.7, 4.9, 1.8],\n",
       "        [6.7, 3.3, 5.7, 2.1],\n",
       "        [7.2, 3.2, 6. , 1.8],\n",
       "        [6.2, 2.8, 4.8, 1.8],\n",
       "        [6.1, 3. , 4.9, 1.8],\n",
       "        [6.4, 2.8, 5.6, 2.1],\n",
       "        [7.2, 3. , 5.8, 1.6],\n",
       "        [7.4, 2.8, 6.1, 1.9],\n",
       "        [7.9, 3.8, 6.4, 2. ],\n",
       "        [6.4, 2.8, 5.6, 2.2],\n",
       "        [6.3, 2.8, 5.1, 1.5],\n",
       "        [6.1, 2.6, 5.6, 1.4],\n",
       "        [7.7, 3. , 6.1, 2.3],\n",
       "        [6.3, 3.4, 5.6, 2.4],\n",
       "        [6.4, 3.1, 5.5, 1.8],\n",
       "        [6. , 3. , 4.8, 1.8],\n",
       "        [6.9, 3.1, 5.4, 2.1],\n",
       "        [6.7, 3.1, 5.6, 2.4],\n",
       "        [6.9, 3.1, 5.1, 2.3],\n",
       "        [5.8, 2.7, 5.1, 1.9],\n",
       "        [6.8, 3.2, 5.9, 2.3],\n",
       "        [6.7, 3.3, 5.7, 2.5],\n",
       "        [6.7, 3. , 5.2, 2.3],\n",
       "        [6.3, 2.5, 5. , 1.9],\n",
       "        [6.5, 3. , 5.2, 2. ],\n",
       "        [6.2, 3.4, 5.4, 2.3],\n",
       "        [5.9, 3. , 5.1, 1.8]]),\n",
       " 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]),\n",
       " 'target_names': array(['setosa', 'versicolor', 'virginica'], dtype='<U10'),\n",
       " 'DESCR': '.. _iris_dataset:\\n\\nIris plants dataset\\n--------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 150 (50 in each of three classes)\\n    :Number of Attributes: 4 numeric, predictive attributes and the class\\n    :Attribute Information:\\n        - sepal length in cm\\n        - sepal width in cm\\n        - petal length in cm\\n        - petal width in cm\\n        - class:\\n                - Iris-Setosa\\n                - Iris-Versicolour\\n                - Iris-Virginica\\n                \\n    :Summary Statistics:\\n\\n    ============== ==== ==== ======= ===== ====================\\n                    Min  Max   Mean    SD   Class Correlation\\n    ============== ==== ==== ======= ===== ====================\\n    sepal length:   4.3  7.9   5.84   0.83    0.7826\\n    sepal width:    2.0  4.4   3.05   0.43   -0.4194\\n    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\\n    petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\\n    ============== ==== ==== ======= ===== ====================\\n\\n    :Missing Attribute Values: None\\n    :Class Distribution: 33.3% for each of 3 classes.\\n    :Creator: R.A. Fisher\\n    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\\n    :Date: July, 1988\\n\\nThe famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\\nfrom Fisher\\'s paper. Note that it\\'s the same as in R, but not as in the UCI\\nMachine Learning Repository, which has two wrong data points.\\n\\nThis is perhaps the best known database to be found in the\\npattern recognition literature.  Fisher\\'s paper is a classic in the field and\\nis referenced frequently to this day.  (See Duda & Hart, for example.)  The\\ndata set contains 3 classes of 50 instances each, where each class refers to a\\ntype of iris plant.  One class is linearly separable from the other 2; the\\nlatter are NOT linearly separable from each other.\\n\\n.. topic:: References\\n\\n   - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\\n     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\\n     Mathematical Statistics\" (John Wiley, NY, 1950).\\n   - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\\n     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\\n   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\\n     Structure and Classification Rule for Recognition in Partially Exposed\\n     Environments\".  IEEE Transactions on Pattern Analysis and Machine\\n     Intelligence, Vol. PAMI-2, No. 1, 67-71.\\n   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\\n     on Information Theory, May 1972, 431-433.\\n   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\\n     conceptual clustering system finds 3 classes in the data.\\n   - Many, many more ...',\n",
       " 'feature_names': ['sepal length (cm)',\n",
       "  'sepal width (cm)',\n",
       "  'petal length (cm)',\n",
       "  'petal width (cm)'],\n",
       " 'filename': 'D:\\\\Programing\\\\Anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\datasets\\\\data\\\\iris.csv'}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import export_graphviz\n",
    "\n",
    "export_graphviz(dtc,\n",
    "                out_file='iris_tree.dot',\n",
    "                feature_names=iris.feature_names[2:],\n",
    "                class_names=iris.target_names,\n",
    "                rounded=True,\n",
    "                filled=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "然后，你可以使用graphviz包中的dot命令行工具将此**.dot**文件转换为各种格式，如PDF或PNG。下面这条命令行将.dot文件转换为.png图像文件：\n",
    "\n",
    "$ dot -Tpng iris_tree.dot -o iris_tree.png"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 6,
     "metadata": {
      "image/png": {
       "height": 600,
       "width": 400
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='iris_tree.png', width=400, height=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 决策边界展示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Decision Tree decision boundaries')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "\n",
    "def plot_decision_boundary(clf,\n",
    "                           X,\n",
    "                           y,\n",
    "                           axes=[0, 7.5, 0, 3],\n",
    "                           iris=True,\n",
    "                           legend=False,\n",
    "                           plot_training=True):\n",
    "    x1s = np.linspace(axes[0], axes[1], 100)\n",
    "    x2s = np.linspace(axes[2], axes[3], 100)\n",
    "    x1, x2 = np.meshgrid(x1s, x2s)\n",
    "    X_new = np.c_[x1.ravel(), x2.ravel()]\n",
    "    y_pred = clf.predict(X_new).reshape(x1.shape)\n",
    "    custom_cmap = ListedColormap(['#fafab0', '#9898ff', '#a0faa0'])\n",
    "    plt.contourf(x1, x2, y_pred, alpha=0.3, cmap=custom_cmap)\n",
    "    if not iris:\n",
    "        custom_cmap2 = ListedColormap(['#7d7d58', '#4c4c7f', '#507d50'])\n",
    "        plt.contour(x1, x2, y_pred, cmap=custom_cmap2, alpha=0.8)\n",
    "    if plot_training:\n",
    "        plt.plot(X[:, 0][y == 0], X[:, 1][y == 0], \"yo\", label=\"Iris-Setosa\")\n",
    "        plt.plot(X[:, 0][y == 1],\n",
    "                 X[:, 1][y == 1],\n",
    "                 \"bs\",\n",
    "                 label=\"Iris-Versicolor\")\n",
    "        plt.plot(X[:, 0][y == 2], X[:, 1][y == 2], \"g^\", label=\"Iris-Virginica\")\n",
    "        plt.axis(axes)\n",
    "    if iris:\n",
    "        plt.xlabel(\"Petal length\", fontsize=14)\n",
    "        plt.ylabel(\"Petal width\", fontsize=14)\n",
    "    else:\n",
    "        plt.xlabel(r\"$x_1$\", fontsize=18)\n",
    "        plt.ylabel(r\"$x_2$\", fontsize=18, rotation=0)\n",
    "    if legend:\n",
    "        plt.legend(loc=\"lower right\", fontsize=14)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(8, 4))\n",
    "plot_decision_boundary(dtc, X, y)\n",
    "plt.plot([2.45, 2.45], [0, 3], \"k-\", linewidth=2)\n",
    "plt.plot([2.45, 7.5], [1.75, 1.75], \"k--\", linewidth=2)\n",
    "plt.plot([4.95, 4.95], [0, 1.75], \"k:\", linewidth=2)\n",
    "plt.plot([4.85, 4.85], [1.75, 3], \"k:\", linewidth=2)\n",
    "plt.text(1.40, 1.0, \"Depth=0\", fontsize=15)\n",
    "plt.text(3.2, 1.80, \"Depth=1\", fontsize=13)\n",
    "plt.text(4.05, 0.5, \"(Depth=2)\", fontsize=11)\n",
    "plt.title('Decision Tree decision boundaries')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 概率估计"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**估计类概率**\n",
    "输入数据为：花瓣长5厘米，宽1.5厘米的花。 相应的叶节点是深度为2的左节点，因此决策树应输出以下概率：\n",
    "\n",
    "* Iris-Setosa 为 0％（0/54），\n",
    "* Iris-Versicolor 为 90.7％（49/54），\n",
    "* Iris-Virginica 为 9.3％（5/54）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 0.90740741, 0.09259259]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtc.predict_proba([[5, 1.5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtc.predict([[5, 1.5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 0.02173913, 0.97826087]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtc.predict_proba([[6, 1.8]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtc.predict([[6, 1.8]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 决策树中的正则化\n",
    "\n",
    "**DecisionTreeClassifier类**还有一些其他参数类似地限制了决策树的形状：\n",
    "\n",
    "* min_samples_split（节点在分割之前必须具有的最小样本数），\n",
    "\n",
    "* min_samples_leaf（叶子节点必须具有的最小样本数），\n",
    "\n",
    "* max_leaf_nodes（叶子节点的最大数量），\n",
    "\n",
    "* max_features（在每个节点处评估用于拆分的最大特征数）。\n",
    "\n",
    "* max_depth(树最大的深度)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                       max_depth=None, max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=4, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
       "                       random_state=13, splitter='best')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import make_moons\n",
    "X, y = make_moons(n_samples=100, noise=0.25, random_state=12)\n",
    "dtc1 = DecisionTreeClassifier(random_state=13)\n",
    "dtc2 = DecisionTreeClassifier(min_samples_leaf=4, random_state=13)\n",
    "dtc1.fit(X, y)\n",
    "dtc2.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'min_samples_leaf=4')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 4))\n",
    "plt.subplot(121)\n",
    "plot_decision_boundary(dtc1, X, y, axes=[-1.5, 2.5, -1, 1.5], iris=False)\n",
    "plt.title('No restrictions')\n",
    "\n",
    "plt.subplot(122)\n",
    "plot_decision_boundary(dtc2, X, y, axes=[-1.5, 2.5, -1, 1.5], iris=False)\n",
    "plt.title('min_samples_leaf=4')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 对数据的敏感"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(6)\n",
    "Xs = np.random.rand(100, 2) - 0.5\n",
    "ys = (Xs[:, 0] > 0).astype(np.float32) * 2\n",
    "\n",
    "angle = np.pi / 4\n",
    "rotaion_matrix = np.array([[np.cos(angle), -np.sin(angle)],\n",
    "                           [np.sin(angle), np.cos(angle)]])\n",
    "Xsr = Xs.dot(rotaion_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Sensitivity to training set rotation')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dtc3 = DecisionTreeClassifier(random_state=12)\n",
    "dtc3.fit(Xs, ys)\n",
    "dtc4 = DecisionTreeClassifier(random_state=12)\n",
    "dtc4.fit(Xsr, ys)\n",
    "\n",
    "plt.figure(figsize=(12, 4))\n",
    "plt.subplot(121)\n",
    "plot_decision_boundary(dtc3, Xs, ys, axes=[-1, 1, -1, 1], iris=False)\n",
    "plt.title('Sensitivity to training no rotation')\n",
    "\n",
    "plt.subplot(122)\n",
    "plot_decision_boundary(dtc4, Xsr, ys, axes=[-1, 1, -1, 1], iris=False)\n",
    "plt.title('Sensitivity to training set rotation')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 回归任务"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(12)\n",
    "m = 200\n",
    "X = np.random.rand(m, 1)\n",
    "y = 4 * (X - 0.5)**2\n",
    "y = y + np.random.randn(m, 1) / 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', max_depth=2,\n",
       "                      max_features=None, max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, presort='deprecated',\n",
       "                      random_state=None, splitter='best')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "dtr = DecisionTreeRegressor(max_depth=2)\n",
    "dtr.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "export_graphviz(dtr,\n",
    "                out_file=('regression_tree.dot'),\n",
    "                feature_names=['x1'],\n",
    "                rounded=True,\n",
    "                filled=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 23,
     "metadata": {
      "image/png": {
       "height": 500,
       "width": 500
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 你的第二个决策树长这样\n",
    "from IPython.display import Image\n",
    "Image(filename='regression_tree.png', width=500, height=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对比树的深度对结果的影响"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', max_depth=3,\n",
       "                      max_features=None, max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, presort='deprecated',\n",
       "                      random_state=12, splitter='best')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtr1 = DecisionTreeRegressor(random_state=12, max_depth=2)\n",
    "dtr2 = DecisionTreeRegressor(random_state=12, max_depth=3)\n",
    "dtr1.fit(X, y)\n",
    "dtr2.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_regression_predictions(tree_reg,\n",
    "                                X,\n",
    "                                y,\n",
    "                                axes=[0, 1, -0.2, 1],\n",
    "                                ylabel=\"$y$\"):\n",
    "    x1 = np.linspace(axes[0], axes[1], 500).reshape(-1, 1)\n",
    "    y_pred = tree_reg.predict(x1)\n",
    "    plt.axis(axes)\n",
    "    plt.xlabel(\"$x_1$\", fontsize=18)\n",
    "    if ylabel:\n",
    "        plt.ylabel(ylabel, fontsize=18, rotation=0)\n",
    "    plt.plot(X, y, \"b.\")\n",
    "    plt.plot(x1, y_pred, \"r.-\", linewidth=2, label=r\"$\\hat{y}$\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'max_depth=3')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 4))\n",
    "\n",
    "plt.subplot(121)\n",
    "plot_regression_predictions(dtr1, X, y)\n",
    "for split, style in ((0.17, 'k-'), (0.07, 'k--'), (0.81, 'k--')):\n",
    "    plt.plot([split, split], [-0.2, 1], style, linewidth=2)\n",
    "\n",
    "plt.text(0.21, 0.65, \"Depth=0\", fontsize=15)\n",
    "plt.text(0.01, 0.2, \"Depth=1\", fontsize=13)\n",
    "plt.text(0.65, 0.8, \"Depth=1\", fontsize=13)\n",
    "plt.legend(loc=\"upper center\", fontsize=18)\n",
    "plt.title(\"max_depth=2\", fontsize=14)\n",
    "\n",
    "plt.subplot(122)\n",
    "plot_regression_predictions(dtr2, X, y)\n",
    "for split, style in ((0.17, 'k-'), (0.07, 'k--'), (0.81, 'k--')):\n",
    "    plt.plot([split, split], [-0.2, 1], style, linewidth=2)\n",
    "for split in (0.015, 0.12, 0.31, 0.91):\n",
    "    plt.plot([split, split], [-0.2, 1], 'k:', linewidth=1)\n",
    "\n",
    "plt.text(0.3, 0.5, \"Depth=2\", fontsize=13)\n",
    "plt.title(\"max_depth=3\", fontsize=14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtr3 = DecisionTreeRegressor(random_state=12)\n",
    "dtr4 = DecisionTreeRegressor(random_state=12, min_samples_leaf=10)\n",
    "dtr3.fit(X, y)\n",
    "dtr4.fit(X, y)\n",
    "\n",
    "x1 = np.linspace(0, 1, 200).reshape(-1, 1)\n",
    "y_pred3 = dtr3.predict(x1)\n",
    "y_pred4 = dtr4.predict(x1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'min_samples_leaf=10')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 4))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.plot(X, y, 'b.')\n",
    "plt.plot(x1, y_pred3, 'r.-', linewidth=2, label=r'$\\hat{y}$')\n",
    "plt.axis([0, 1, -0.2, 1.1])\n",
    "plt.xlabel('$x_1$', fontsize=18)\n",
    "plt.ylabel('$y$', fontsize=18, rotation=0)\n",
    "plt.legend(loc='upper center', fontsize=18)\n",
    "plt.title('No restrictions', fontsize=14)\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(X, y, \"b.\")\n",
    "plt.plot(x1, y_pred4, \"r.-\", linewidth=2, label=r\"$\\hat{y}$\")\n",
    "plt.axis([0, 1, -0.2, 1.1])\n",
    "plt.xlabel(\"$x_1$\", fontsize=18)\n",
    "plt.title(\"min_samples_leaf={}\".format(dtr4.min_samples_leaf), fontsize=14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
